Learn More
We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task. We first present a computational formulation of the robot's interrole knowledge and(More)
We present a framework for automatically learning human user models from joint-action demonstrations that enables a robot to compute a robust policy for a collaborative task with a human. First, the demonstrated action sequences are clustered into different human types using an unsupervised learning algorithm. A reward function is then learned for each type(More)
In this paper, we propose an object localization method for home environments. This method utilizes RFID equipments, a mobile robot and some ceiling cameras. The RFID system estimates a rough position of each object. The autonomous robot with RFID antennas explores the environment so as to detect other objects on the floor. Each object that is attached an(More)
Adaptation is critical for effective team collaboration. This paper introduces a computational formalism for mutual adaptation between a robot and a human in collaborative tasks. We propose the Bounded-Memory Adaptation Model, which is a probabilistic finite-state controller that captures human adaptive behaviors under a bounded-memory assumption. We(More)
—Human-robot collaboration presents an opportunity to improve the efficiency of manufacturing and assembly processes , particularly for aerospace manufacturing where tight integration and variability in the build process make physical isolation of robotic-only work challenging. In this paper, we develop a robotic scheduling and control capability that(More)
A power consumption measurement framework for embedded processing systems is presented in this work. Given an assembly or machine level program as input to this setup, the energy consumption of the specific program in the specific processing systems may be estimated. The instruction level power models are derived based on the power supply current(More)
New industrial robotic systems that operate in the same physical space as people highlight the emerging need for robots that can integrate seamlessly into human group dynamics. In this paper we build on our prior investigation, which evaluates the convergence of a robot computational teaming model and a human teammate's mental model, by computing the(More)
This paper describes an approach on real-time performance 3D visualization in the context of music education. A tool is described that produces sound visualizations during a student performance that are intuitively linked to common mistakes frequently observed in the performances of novice to intermediate students. The paper discusses the case of clarinet(More)
—Mutual adaptation is critical for effective team collaboration. This paper presents a formalism for human-robot mutual adaptation in collaborative tasks. We propose the bounded-memory adaptation model (BAM), which captures human adaptive behaviors based on a bounded memory assumption. We integrate BAM into a partially observable stochastic model, which(More)